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Значимость клинико-лабораторных индексов в диагностике неалкогольной жировой болезни печени
Значимость клинико-лабораторных индексов в диагностике неалкогольной жировой болезни печени
Носов А.Е., Зенина М.Т., Горбушина О.Ю., Байдина А.С., Власова Е.М., Алексеев В.Б. Значимость клинико-лабораторных индексов в диагностике неалкогольной жировой болезни печени. Терапевтический архив. 2021; 93 (8): 883–889.
DOI: 10.26442/00403660.2021.08.200973
DOI: 10.26442/00403660.2021.08.200973
DOI: 10.26442/00403660.2021.08.200973
________________________________________________
DOI: 10.26442/00403660.2021.08.200973
Материалы доступны только для специалистов сферы здравоохранения. Авторизуйтесь или зарегистрируйтесь.
Аннотация
Цель. Изучить значимость клинико-лабораторных неинвазивных индексов в сочетании с индексом инсулинорезистентности в диагностике неалкогольной жировой болезни печени (НАЖБП) при скрининговых обследованиях.
Материалы и методы. В исследование включены 348 работников нефтедобывающего предприятия. Выполнялось ультразвуковое сканирование (УЗИ) печени с оценкой критериев НАЖБП. Вычислялись индексы: fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation products (LAP), индекс инсулинорезистентности (HOMA1-IR). Прогностическая значимость данных индексов в отношении вероятности диагностики НАЖБП по данным УЗИ изучалась в моделях однофакторной и многофакторной логистической регрессии с выполнением ROC-анализа.
Результаты. Индексы FLI, HSI и HOMA1-IR в моделях однофакторной логистической регрессии показали высокую статистическую значимость в диагностике НАЖБП с хорошей калибрационной способностью моделей. Доля правильной бинарной классификации в отношении наличия/отсутствия НАЖБП составила для FLI 82,4%, для HSI – 79,7%, а для HOMA1-IR – 72,7% (р<0,001). По данным ROC-анализа, площадь под кривой (AUC) при диагностике НАЖБП составила для этих индексов 0,917 (95% доверительный интервал – ДИ 0,889–0,945), 0,880 (95% ДИ 0,846–0,915) и 0,849 (95% ДИ 0,764–0,934) соответственно. Многофакторная логистическая регрессионная модель с включением FLI и HOMA1-IR позволила достичь правильной бинарной классификации в отношении НАЖБП в 84,2% случаев, а в ROC-анализе на основании предсказанных в многофакторной логистической модели вероятностей в качестве тестируемой переменной и НАЖБП при УЗИ в качестве переменной состояния установили значение AUC=0,933 (95% ДИ 0,882–0,985).
Заключение. Изученные клинико-лабораторные индексы (FLI, HSI, HOMA1-IR) обладают высокой диагностической значимостью в отношении НАЖБП, установленной по ультрасонографическим критериям. Применение предложенной двухфакторной логистической модели позволяет в условиях массового обследования достаточно точно прогнозировать наличие НАЖБП без дополнительного широкого привлечения специалистов ультразвуковой диагностики в целях рационального использования медицинских ресурсов.
Ключевые слова: неалкогольная жировая болезнь печени, ультразвуковое сканирование печени, инсулинорезистентность, индексы стеатоза
Materials and methods. The study involved 348 employees working at oil-production enterprises. An ultrasound scanning of the liver was carried out to assess the criteria of NAFLD. The following indexes were calculated: fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation products (LAP), and homeostasis model assessment of insulin resistance (HOMA1-IR). The prognostic significance of these indexes in relation to the probability of NAFLD diagnosis based on ultrasound data was studied using single-factor and multi-factor logistic regression models followed by ROC-analysis.
Results. The FLI, HSI, and HOMA1-IR indexes in single-factor logistic regression models showed a high statistical significance when carrying out diagnostic assessment the NAFLD with good model calibration capability. The percentage of correct binary classification regards the presence/absence of NAFLD amounted to 82.4% for FLI, 79.7% for HSI, and 72.7% for HOMA1-IR (p<0.001). According to the ROC-analysis, the area under the curve (AUC) by the NAFLD diagnostic assessment was 0.917 (95% CI 0.889–0.945); 0.880 (95% CI 0.846–0.915) and 0.849 (95% CI 0.764–0.934), respectively. The multi-factor logistic regression model with the inclusion of FLI and HOMA1-IR 72.7% enabled us to achieve the correct binary classification in terms of NAFLD in 84.2% of cases. When it comes to the ROC-analysis, considering the probabilities predicted in the multi-factor logistic model as the test variable and NAFLD in ultrasound examination as the state variable, it was possible to set the value of AUC 0.933 (95% CI 0.882–0.985).
Conclusion. The studied clinical and laboratory indexes (FLI, HSI, HOMA1-IR) have a high diagnostic significance regarding NAFLD diagnosed using ultrasonographic criteria. The application of the proposed two-factor logistics model makes it possible to predict the presence of NAFLD when examining a large number of patients, without involving additional ultrasound diagnostics specialists in order to use medical resources rationally.
Keywords: non-alcoholic fatty liver disease, ultrasound scanning of the liver, insulin resistance, steatosis indexes
Материалы и методы. В исследование включены 348 работников нефтедобывающего предприятия. Выполнялось ультразвуковое сканирование (УЗИ) печени с оценкой критериев НАЖБП. Вычислялись индексы: fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation products (LAP), индекс инсулинорезистентности (HOMA1-IR). Прогностическая значимость данных индексов в отношении вероятности диагностики НАЖБП по данным УЗИ изучалась в моделях однофакторной и многофакторной логистической регрессии с выполнением ROC-анализа.
Результаты. Индексы FLI, HSI и HOMA1-IR в моделях однофакторной логистической регрессии показали высокую статистическую значимость в диагностике НАЖБП с хорошей калибрационной способностью моделей. Доля правильной бинарной классификации в отношении наличия/отсутствия НАЖБП составила для FLI 82,4%, для HSI – 79,7%, а для HOMA1-IR – 72,7% (р<0,001). По данным ROC-анализа, площадь под кривой (AUC) при диагностике НАЖБП составила для этих индексов 0,917 (95% доверительный интервал – ДИ 0,889–0,945), 0,880 (95% ДИ 0,846–0,915) и 0,849 (95% ДИ 0,764–0,934) соответственно. Многофакторная логистическая регрессионная модель с включением FLI и HOMA1-IR позволила достичь правильной бинарной классификации в отношении НАЖБП в 84,2% случаев, а в ROC-анализе на основании предсказанных в многофакторной логистической модели вероятностей в качестве тестируемой переменной и НАЖБП при УЗИ в качестве переменной состояния установили значение AUC=0,933 (95% ДИ 0,882–0,985).
Заключение. Изученные клинико-лабораторные индексы (FLI, HSI, HOMA1-IR) обладают высокой диагностической значимостью в отношении НАЖБП, установленной по ультрасонографическим критериям. Применение предложенной двухфакторной логистической модели позволяет в условиях массового обследования достаточно точно прогнозировать наличие НАЖБП без дополнительного широкого привлечения специалистов ультразвуковой диагностики в целях рационального использования медицинских ресурсов.
Ключевые слова: неалкогольная жировая болезнь печени, ультразвуковое сканирование печени, инсулинорезистентность, индексы стеатоза
________________________________________________
Materials and methods. The study involved 348 employees working at oil-production enterprises. An ultrasound scanning of the liver was carried out to assess the criteria of NAFLD. The following indexes were calculated: fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation products (LAP), and homeostasis model assessment of insulin resistance (HOMA1-IR). The prognostic significance of these indexes in relation to the probability of NAFLD diagnosis based on ultrasound data was studied using single-factor and multi-factor logistic regression models followed by ROC-analysis.
Results. The FLI, HSI, and HOMA1-IR indexes in single-factor logistic regression models showed a high statistical significance when carrying out diagnostic assessment the NAFLD with good model calibration capability. The percentage of correct binary classification regards the presence/absence of NAFLD amounted to 82.4% for FLI, 79.7% for HSI, and 72.7% for HOMA1-IR (p<0.001). According to the ROC-analysis, the area under the curve (AUC) by the NAFLD diagnostic assessment was 0.917 (95% CI 0.889–0.945); 0.880 (95% CI 0.846–0.915) and 0.849 (95% CI 0.764–0.934), respectively. The multi-factor logistic regression model with the inclusion of FLI and HOMA1-IR 72.7% enabled us to achieve the correct binary classification in terms of NAFLD in 84.2% of cases. When it comes to the ROC-analysis, considering the probabilities predicted in the multi-factor logistic model as the test variable and NAFLD in ultrasound examination as the state variable, it was possible to set the value of AUC 0.933 (95% CI 0.882–0.985).
Conclusion. The studied clinical and laboratory indexes (FLI, HSI, HOMA1-IR) have a high diagnostic significance regarding NAFLD diagnosed using ultrasonographic criteria. The application of the proposed two-factor logistics model makes it possible to predict the presence of NAFLD when examining a large number of patients, without involving additional ultrasound diagnostics specialists in order to use medical resources rationally.
Keywords: non-alcoholic fatty liver disease, ultrasound scanning of the liver, insulin resistance, steatosis indexes
Полный текст
Список литературы
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5. Byrne CD, Targher G. NAFLD: A multisystem disease. J Hepatol. 2015;62(1):47-64. DOI:10.1016/j.jhep.2014.12.012
6. Mantovani A, Zaza G, Byrne CD, et al. Nonalcoholic fatty liver disease increases risk of incident chronic kidney disease: A systematic review and meta-analysis. Metabolism. 2018;79:64-76. DOI:10.1016/j.metabol.2017.11.003
7. Оганов Р.Г., Симаненков В.И., Бакулин И.Г., и др. Коморбидная патология в клинической практике. Кардиоваскулярная терапия и профилактика. 2019;18(1):5-66 [Oganov RG, Simanenkov VI, Bakulin IG, et al. Comorbidities in clinical practice. Algorithms for diagnostics and treatment. Cardiovascular Therapy and Prevention. 2019;(1):5-66 (in Russian)]. DOI:10.15829/1728-8800-2019-1-5-66
8. Практическое руководство по ультразвуковой диагностике. Общая ультразвуковая диагностика. Под ред. В.В. Митькова. М.: Видар-М, 2005
[A practical guide to ultrasound diagnostics. General ultrasound diagnostics. Ed. VV Mitkov. Moscow: Vidar-M, 2005 (in Russian)].
9. Li X, Zhou Z, Qi H, et al. Replacement of insulin by fasting C-peptide in modified homeostasis model assessment to evaluate insulin resistance and islet beta cell function. Journal of Central South University. Medical sciences. 2004;29(4):419-23
10. Basukala P, Jha B, Yadav BK, Shrestha PK. Determination of Insulin Resistance and Beta-Cell Function Using Homeostatic Model Assessment in Type 2 Diabetic Patients at Diagnosis. Diabetes Metab J. 2018;9(3):790. DOI:10.4172/2155-6156.1000790
11. Валеева В.Ф., Нуруллина Г.И. Диагностическая ценность С-пептида и модифицированных индексов HOMA при различных нарушениях углеводного обмена на фоне терапии глюкокортикоидами. Мед. вестн. юга России. 2018;9(1):23-31 [Valeeva FV, Nurullina GI. C-peptide and modified HOMA-index in different carbohydrate metabolism disturbances during glucocorticoid therapy. Medical Herald of the South of Russia. 2018;9(1):23-31 (in Russian)]. DOI:10.21886/2219-8075-2018-9-1-23-31
12. Nurullina G. The role of HOMA-IR and HOMA_ISLET indices in different carbohydrate metabolism disorders during glucocorticoid therapy. Ann Rheum Dis. 2017;76:1161. DOI:10.1136/annrheumdis-2017-eular.5721
13. Browning JD, Szczepaniak LS, Dobbins R, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology. 2004;40:1387-95. DOI:10.1002/hep.20466
14. Prati D, Taioli E, Zanella A, et al. Updated deinitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med. 2002;137:1-10.
DOI:10.7326/0003-4819-137-1-200207020-00006
15. Ballestri S, Nascimbeni F, Baldelli E, et al. Ultrasonographic fatty liver indicator detects mild steatosis and correlates with metabolic/histological parameters in various liver diseases. Metabolism. 2017;72:57-65. DOI:10.1016/j.metabol.2017.04.003
16. Bedogni G, Bellentani S, Miglioli L, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6(33). DOI:10.1186/1471-230X-6-33
17. Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid overaccumulation is a good marker of liver steatosis. BMC Gastroenterol. 2010;10(98).
DOI:10.1186/1471-230X-10-98
18. Xia C, Li R, Zhang S, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur J Clin Nutr. 2012;66(9):1035-8. DOI:10.1038/ejcn.2012.83
19. Zhu J, He M, Zhang Y, et al. Validation of simple indexes for nonalcoholic fatty liver disease in western China: a retrospective cross-sectional study. Endocr J. 2018;65(3):373-81. DOI:10.1507/endocrj.EJ17-0466
20. Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:503-8. DOI:10.1016/j.dld.2009.08.002
21. Cuthbertson DJ, Weickert MO, Lythgoe D, et al. External validation of the fatty liver index and lipid accumulation product indices, using 1H-magnetic resonance spectroscopy, to identify hepatic steatosis in healthy controls and obese, insulin-resistant individuals. Eur J Endocrinol. 2014;171(5):561-9. DOI:10.1530/EJE-14-0112
22. Huang X, Xu M, Chen Y, et al. Validation of the Fatty Liver Index for Nonalcoholic Fatty Liver Disease in Middle-Aged and Elderly Chinese. Medicine. 2015;94(40):e1682. DOI:10.1097/MD.0000000000001682
23. Yang BL, Wu WC, Fang KC, et al. External validation of fatty liver index for identifying ultrasonographic fatty liver in a large-scale cross-sectional study in Taiwan. PLoS One. 2015;10(3):e0120443. DOI:10.1371/journal.pone.0120443
DOI:10.1136/postgradmedj-2018-136316
2. European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease. Obesity Facts. 2016;9(2):65-90. DOI:10.1159/000443344
3. Singh S, Allen AM, Wang Z, et al. Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired-biopsy studies. Clinical Gastroenterol Hepatol. 2015;13:643-54. DOI:10.1016/j.cgh.2014.04.014
4. Targher G, Byrne CD, Lonardo A, et al. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis.
J Hepatol. 2016;65(3):589-600. DOI:10.1016/j.jhep.2016.05.013
5. Byrne CD, Targher G. NAFLD: A multisystem disease. J Hepatol. 2015;62(1):47-64. DOI:10.1016/j.jhep.2014.12.012
6. Mantovani A, Zaza G, Byrne CD, et al. Nonalcoholic fatty liver disease increases risk of incident chronic kidney disease: A systematic review and meta-analysis. Metabolism. 2018;79:64-76. DOI:10.1016/j.metabol.2017.11.003
7. Oganov RG, Simanenkov VI, Bakulin IG, et al. Comorbidities in clinical practice. Algorithms for diagnostics and treatment. Cardiovascular Therapy and Prevention. 2019;(1):5-66 (in Russian) DOI:10.15829/1728-8800-2019-1-5-66
8. A practical guide to ultrasound diagnostics. General ultrasound diagnostics. Ed. VV Mitkov. Moscow: Vidar-M, 2005 (in Russian)
9. Li X, Zhou Z, Qi H, et al. Replacement of insulin by fasting C-peptide in modified homeostasis model assessment to evaluate insulin resistance and islet beta cell function. Journal of Central South University. Medical sciences. 2004;29(4):419-23
10. Basukala P, Jha B, Yadav BK, Shrestha PK. Determination of Insulin Resistance and Beta-Cell Function Using Homeostatic Model Assessment in Type 2 Diabetic Patients at Diagnosis. Diabetes Metab J. 2018;9(3):790. DOI:10.4172/2155-6156.1000790
11. Valeeva FV, Nurullina GI. C-peptide and modified HOMA-index in different carbohydrate metabolism disturbances during glucocorticoid therapy. Medical Herald of the South of Russia. 2018;9(1):23-31 (in Russian) DOI:10.21886/2219-8075-2018-9-1-23-31
12. Nurullina G. The role of HOMA-IR and HOMA_ISLET indices in different carbohydrate metabolism disorders during glucocorticoid therapy. Ann Rheum Dis. 2017;76:1161. DOI:10.1136/annrheumdis-2017-eular.5721
13. Browning JD, Szczepaniak LS, Dobbins R, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology. 2004;40:1387-95. DOI:10.1002/hep.20466
14. Prati D, Taioli E, Zanella A, et al. Updated deinitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med. 2002;137:1-10.
DOI:10.7326/0003-4819-137-1-200207020-00006
15. Ballestri S, Nascimbeni F, Baldelli E, et al. Ultrasonographic fatty liver indicator detects mild steatosis and correlates with metabolic/histological parameters in various liver diseases. Metabolism. 2017;72:57-65. DOI:10.1016/j.metabol.2017.04.003
16. Bedogni G, Bellentani S, Miglioli L, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6(33). DOI:10.1186/1471-230X-6-33
17. Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid overaccumulation is a good marker of liver steatosis. BMC Gastroenterol. 2010;10(98).
DOI:10.1186/1471-230X-10-98
18. Xia C, Li R, Zhang S, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur J Clin Nutr. 2012;66(9):1035-8. DOI:10.1038/ejcn.2012.83
19. Zhu J, He M, Zhang Y, et al. Validation of simple indexes for nonalcoholic fatty liver disease in western China: a retrospective cross-sectional study. Endocr J. 2018;65(3):373-81. DOI:10.1507/endocrj.EJ17-0466
20. Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:503-8. DOI:10.1016/j.dld.2009.08.002
21. Cuthbertson DJ, Weickert MO, Lythgoe D, et al. External validation of the fatty liver index and lipid accumulation product indices, using 1H-magnetic resonance spectroscopy, to identify hepatic steatosis in healthy controls and obese, insulin-resistant individuals. Eur J Endocrinol. 2014;171(5):561-9. DOI:10.1530/EJE-14-0112
22. Huang X, Xu M, Chen Y, et al. Validation of the Fatty Liver Index for Nonalcoholic Fatty Liver Disease in Middle-Aged and Elderly Chinese. Medicine. 2015;94(40):e1682. DOI:10.1097/MD.0000000000001682
23. Yang BL, Wu WC, Fang KC, et al. External validation of fatty liver index for identifying ultrasonographic fatty liver in a large-scale cross-sectional study in Taiwan. PLoS One. 2015;10(3):e0120443. DOI:10.1371/journal.pone.0120443
DOI:10.1136/postgradmedj-2018-136316
2. European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease. Obesity Facts. 2016;9(2):65-90. DOI:10.1159/000443344
3. Singh S, Allen AM, Wang Z, et al. Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired-biopsy studies. Clinical Gastroenterol Hepatol. 2015;13:643-54. DOI:10.1016/j.cgh.2014.04.014
4. Targher G, Byrne CD, Lonardo A, et al. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis.
J Hepatol. 2016;65(3):589-600. DOI:10.1016/j.jhep.2016.05.013
5. Byrne CD, Targher G. NAFLD: A multisystem disease. J Hepatol. 2015;62(1):47-64. DOI:10.1016/j.jhep.2014.12.012
6. Mantovani A, Zaza G, Byrne CD, et al. Nonalcoholic fatty liver disease increases risk of incident chronic kidney disease: A systematic review and meta-analysis. Metabolism. 2018;79:64-76. DOI:10.1016/j.metabol.2017.11.003
7. Оганов Р.Г., Симаненков В.И., Бакулин И.Г., и др. Коморбидная патология в клинической практике. Кардиоваскулярная терапия и профилактика. 2019;18(1):5-66 [Oganov RG, Simanenkov VI, Bakulin IG, et al. Comorbidities in clinical practice. Algorithms for diagnostics and treatment. Cardiovascular Therapy and Prevention. 2019;(1):5-66 (in Russian)]. DOI:10.15829/1728-8800-2019-1-5-66
8. Практическое руководство по ультразвуковой диагностике. Общая ультразвуковая диагностика. Под ред. В.В. Митькова. М.: Видар-М, 2005
[A practical guide to ultrasound diagnostics. General ultrasound diagnostics. Ed. VV Mitkov. Moscow: Vidar-M, 2005 (in Russian)].
9. Li X, Zhou Z, Qi H, et al. Replacement of insulin by fasting C-peptide in modified homeostasis model assessment to evaluate insulin resistance and islet beta cell function. Journal of Central South University. Medical sciences. 2004;29(4):419-23
10. Basukala P, Jha B, Yadav BK, Shrestha PK. Determination of Insulin Resistance and Beta-Cell Function Using Homeostatic Model Assessment in Type 2 Diabetic Patients at Diagnosis. Diabetes Metab J. 2018;9(3):790. DOI:10.4172/2155-6156.1000790
11. Валеева В.Ф., Нуруллина Г.И. Диагностическая ценность С-пептида и модифицированных индексов HOMA при различных нарушениях углеводного обмена на фоне терапии глюкокортикоидами. Мед. вестн. юга России. 2018;9(1):23-31 [Valeeva FV, Nurullina GI. C-peptide and modified HOMA-index in different carbohydrate metabolism disturbances during glucocorticoid therapy. Medical Herald of the South of Russia. 2018;9(1):23-31 (in Russian)]. DOI:10.21886/2219-8075-2018-9-1-23-31
12. Nurullina G. The role of HOMA-IR and HOMA_ISLET indices in different carbohydrate metabolism disorders during glucocorticoid therapy. Ann Rheum Dis. 2017;76:1161. DOI:10.1136/annrheumdis-2017-eular.5721
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11. Valeeva FV, Nurullina GI. C-peptide and modified HOMA-index in different carbohydrate metabolism disturbances during glucocorticoid therapy. Medical Herald of the South of Russia. 2018;9(1):23-31 (in Russian) DOI:10.21886/2219-8075-2018-9-1-23-31
12. Nurullina G. The role of HOMA-IR and HOMA_ISLET indices in different carbohydrate metabolism disorders during glucocorticoid therapy. Ann Rheum Dis. 2017;76:1161. DOI:10.1136/annrheumdis-2017-eular.5721
13. Browning JD, Szczepaniak LS, Dobbins R, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology. 2004;40:1387-95. DOI:10.1002/hep.20466
14. Prati D, Taioli E, Zanella A, et al. Updated deinitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med. 2002;137:1-10.
DOI:10.7326/0003-4819-137-1-200207020-00006
15. Ballestri S, Nascimbeni F, Baldelli E, et al. Ultrasonographic fatty liver indicator detects mild steatosis and correlates with metabolic/histological parameters in various liver diseases. Metabolism. 2017;72:57-65. DOI:10.1016/j.metabol.2017.04.003
16. Bedogni G, Bellentani S, Miglioli L, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6(33). DOI:10.1186/1471-230X-6-33
17. Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid overaccumulation is a good marker of liver steatosis. BMC Gastroenterol. 2010;10(98).
DOI:10.1186/1471-230X-10-98
18. Xia C, Li R, Zhang S, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur J Clin Nutr. 2012;66(9):1035-8. DOI:10.1038/ejcn.2012.83
19. Zhu J, He M, Zhang Y, et al. Validation of simple indexes for nonalcoholic fatty liver disease in western China: a retrospective cross-sectional study. Endocr J. 2018;65(3):373-81. DOI:10.1507/endocrj.EJ17-0466
20. Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:503-8. DOI:10.1016/j.dld.2009.08.002
21. Cuthbertson DJ, Weickert MO, Lythgoe D, et al. External validation of the fatty liver index and lipid accumulation product indices, using 1H-magnetic resonance spectroscopy, to identify hepatic steatosis in healthy controls and obese, insulin-resistant individuals. Eur J Endocrinol. 2014;171(5):561-9. DOI:10.1530/EJE-14-0112
22. Huang X, Xu M, Chen Y, et al. Validation of the Fatty Liver Index for Nonalcoholic Fatty Liver Disease in Middle-Aged and Elderly Chinese. Medicine. 2015;94(40):e1682. DOI:10.1097/MD.0000000000001682
23. Yang BL, Wu WC, Fang KC, et al. External validation of fatty liver index for identifying ultrasonographic fatty liver in a large-scale cross-sectional study in Taiwan. PLoS One. 2015;10(3):e0120443. DOI:10.1371/journal.pone.0120443
Авторы
А.Е. Носов*, М.Т. Зенина, О.Ю. Горбушина, А.С. Байдина, Е.М. Власова, В.Б. Алексеев
ФБУН «Федеральный научный центр медико-профилактических технологий управления рисками здоровью населения» Роспотребнадзора, Пермь, Россия
*nosov@fcrisk.ru
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, Perm, Russia
*nosov@fcrisk.ru
ФБУН «Федеральный научный центр медико-профилактических технологий управления рисками здоровью населения» Роспотребнадзора, Пермь, Россия
*nosov@fcrisk.ru
________________________________________________
Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, Perm, Russia
*nosov@fcrisk.ru
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